KMBBO

Efficient and Scalable Batch Bayesian Optimization Using K-Means

Absctract

We present K-Means Batch Bayesian Optimization (KMBBO), a novel batch sampling algorithm for Bayesian Optimization(BO). KMBBO uses unsupervised learning to efficiently es-timate peaks of the model acquisition function. We show inempirical experiments that our method outperforms the current state-of-the-art batch allocation algorithms on a variety oftest problems including tuning of algorithm hyper-parameters and a challenging drug discovery problem. In order to accommodate the real-world problem of high dimensional data, we propose a modification to KMBBO by combining it withcompressed sensing to project the optimization into a lower dimensional subspace. We demonstrate empirically that this 2-step method outperforms algorithms where no dimensionalityreduction has taken place.

Reference

• Efficient and Scalable Batch Bayesian Optimization Using K-Means
• Budgeted Batch Bayesian Optimization With Unknown Batch Sizes
• Python code for bayesian optimization using Gaussian processes
• Gpy vs scikit-learn: pythonでガウス過程回帰

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